There are, of course, problems with this data. I don’t know how Politifact decides which statements to check. The selection process is likely biased in several ways. For example, perhaps controversial or likely-false statements are more likely to become Politifact fodder in the first place. Thus I would be hesitant to conclude that any of the above people are lying X% of the time in general, just because X% of their rulings are False or PantsOnFire. There are a variety of other problems lurking here, especially when the data is thin like for Pelosi and Reid.

Nevertheless, let’s do some math for the fun of it.

In aggregate, 21% of the statements ruled on are TRUE. Boehner is highest at 31% and Pelosi is lowest at 5% (but her data is so thin; one more “true” statement and she would have been 10%). The four Democrats scored 23% in aggregate and the GOP scored 19%.

Expanding to include “mostly true” (and the fully-true), the aggregate is 38%, with Obama best at 47%, and Pelosi and Gingrich bringing up the rear at around 16%. The four Democrats were 44% mostly-true or true, and the GOP was 30%.

False or Pants on Fire is 23% in aggregate. Gingrich is the run away winner here at 42% with Obama the best, of a sorry lot, at 17%. GOP 31%, Democrats 18%.

I also looked at the rulings for the DNC and the RNC. Their numbers:

True Mostly True Half True Mostly False False Pants on Fire

DNC 6 5 8 6 2 0

RNC 2 4 8 5 5 2

This nets out to DNC being Mostly True or True 41% of the time, and the RNC only 23%.

The data is at the very least suggestive that the Republicans, in aggregate, are less truthful than the Democrats though we’d really have to do a more scientific survey (better selection methodology) to draw any real conclusion. Neither party appears to deserve a gold star for veracity.

Today’s (self-imposed) photography project was: take a picture of an orange and convert it to black-and-white. Here’s the original picture of the orange (you can click on the picture to get the full-size version).

I have two different owls up here. One is a Great Horned, one is a Screech Owl. They each live in their respective owl boxes that were put up (there are several all over the property). Most recently observed/identified on a wildlife survey with Plateau Wildlife Mgmt a few days ago. Didn’t get a great look at either one – just saw them flying away as we approached their box.

The nose wheel on my plane has 8 numbers on it, and being degenerate gamblers we bet on which number will end up on the bottom each time we land. Also, being nerds, we keep track of the results. I now have 67 data points from various landings.

The results in sequence: 7,3,6,5,5,6,3,8,6,1,5,6,3,8,4,8,5,3,2,1,3,3,1,4,2,8,7,6,7,4,3,6,5,6,3,2,8,7,3,
8,3,2,5,7,7,2,3,2,3,8,4,5,1,5,4,2,2,1,5,3,1,1,5,3,4,3,5

The numbers are written on the wheel in chalk and are periodically redrawn. The chalk stays on the wheel for a long time, but any rain washes the numbers pretty much completely off. This data has been gathered over several years, and many rechalkings of the wheel. If there is bias in a particular chalking (i.e., if they didn’t get the wheel exactly divided into 8ths) we are presuming that over time (over many chalkings) this gets randomized and/or is not statistically significant. In any case the chi-square test will help ferret out such bias, if any.

Sometimes the wheel lands “too close to call” (right on a line) and we call that a push. That happened 7 times out of 74 landings; on the assumption that the pushes are random events like everything else I have simply taken them out of this data for this analysis (leaving the 67 data points shown).

Now the interesting (to geeks) question is this one: are these data points consistent with the notion that the wheel is random?

There are a variety of ways to test for this. Today we start with the chi-square test.

Our Null Hypothesis: the wheel is random and each number should show up N/8 = 8.375 times.

The actual counts are:

1: 72: 83: 154: 65: 116: 77: 68: 7

The number 5 is heavy, 3 even more, and the others are light. Is this random variation or is there some systematic reason 3 is coming up? Let’s see what chi-square says.

To compute chi-square, we take the sum of ((O-E)**2 / E) … “O” being the observed count, “E” being expected (and “**2” meaning “squared”). This calculation comes out to 8.10.

From that, and the seven degrees of freedom (with eight possible values there are N-1 = 7 degrees of freedom), we can look up “p” which is, roughly, the probability that these variations came from randomness rather than some statistically significant factor. There are also online calculators for this, such as http://www.graphpad.com/quickcalcs/chisquared2.cfm

The calculated “p” value for this data is 0.32. When you hear statisticians say “blah blah blah is not statistically significant” they are usually talking about a chi-square test, and the threshold for significance is having a “p” value of 0.05 or less. Larger than that and we say that it is reasonably likely that the variations come from random factors rather than some specific cause. Our “p” here is quite a bit above 0.05, so the data is consistent with the wheel being random.

Stated the other way around, while the numbers obviously vary from the exact 8.375 count for each value, the amount of the variation is well within the boundaries one would expect from normal randomness (just as when you are in Vegas and see the roulette wheel come up red four times in a row on occasion – it doesn’t mean the wheel is rigged, it’s just one of those streaky things that happens from time to time).

Next scheduled trip is back to Canada in February (I pick the best spots to go in the winter, don’t I?) … action on the wheel can be booked by simply sending me a message – pick your numbers and your amounts!